CN111431962A - Cross-domain resource access Internet of things service discovery method based on context awareness calculation - Google Patents
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Abstract
The invention provides a method and a device for discovering cross-domain resource access Internet of things service based on context awareness computing, electronic equipment and a non-transitory computer readable storage medium, wherein the method comprises the following steps: receiving service request information; determining at least one entity in a target knowledge graph model responding to the service request information according to the service request information, wherein the target knowledge graph model represents an entity relationship network formed by the entities and corresponding relationships among the entities; and then acquiring a service set table, and further indexing at least one service set corresponding to the at least one entity in the service set table. The method can improve the service discovery level, reduce the error of service discovery and improve the service discovery effect.
Description
Technical Field
The invention relates to the technical field of internet of things, in particular to a cross-domain resource access internet of things service discovery method and device based on context-aware computing, electronic equipment and a non-transitory computer-readable storage medium.
Background
The Internet of things (IoT) is an Internet with connected objects, and extends the Internet into the physical world, and connects objects in the physical world with the Internet according to an agreed protocol by using Radio Frequency Identification (RFID), sensors, global positioning and other information sensing technologies to perform information exchange and communication, thereby realizing intelligent identification, positioning, tracking, monitoring and management of the objects.
With the rapid development of the technologies of the RFID, the wireless sensing technology, the embedded device and the mobile intelligent device and the cheapness of the physical devices, more and more physical devices are connected to the Internet of things, and data provided by the service of the Internet of things starts to develop towards large scale, multi-source isomerism, cross-field, cross-media, cross-language, dynamic evolution and pervasion. It is becoming more and more important how to find a service with specific functions from a large number of heterogeneous devices with limited resources to meet the needs of users. By utilizing a big data technology, the relevance, the intersection and the fusion of data are realized, the value of the data is maximized, and therefore the establishment of an efficient, rapid and automatic intelligent Internet of things service discovery framework capable of carrying out classification management on services is a problem of major attention of the Internet of things industry.
At present, most of intelligent service discovery is based on a semantic service similarity calculation mode, services are classified, so that the space required for searching for service discovery is reduced, and the service providing efficiency is improved, such as some probability topic models. One scenario in which the above model can be implemented: when a request for a nearby hospital is received, the model may face the situation of completely lacking ontology world knowledge, and the information of all hospitals is fed back; or with the support of simple geographical knowledge, return to the nearest hospital in european style, but perhaps there is no direct path to reach or the path is far away. This is not intended by the service request originator. The above scenario is just a problem caused by lack of spatial information, and in reality, the service request of us may also have a need for information such as time and social relation knowledge.
Due to the lack of relevant knowledge about the ontology world and the absence of reasoning functions, the model is no longer suitable for service discovery of cross-domain and large-scale data, and in addition, the model can have large errors.
Disclosure of Invention
In view of the above, the present invention provides a method and an apparatus for discovering a service accessing an internet of things through a cross-domain resource based on context-aware computing, an electronic device, and a non-transitory computer-readable storage medium, so as to solve the problems of low service discovery level, large error, and poor effect in the prior art.
Based on the above purpose, the invention provides a method for discovering cross-domain resource access internet of things service based on context awareness calculation, which comprises the following steps:
receiving service request information;
determining at least one entity in a target knowledge graph model responding to the service request information according to the service request information, wherein the target knowledge graph model represents an entity relationship network formed by the entities and corresponding relationships among the entities;
acquiring a service set table;
indexing at least one service set corresponding to the at least one entity in the service set table.
In one embodiment, the determining, according to the service request information, at least one entity responding to the service request information includes:
extracting basic condition information and a request object in the service request information;
and determining at least one entity responding to the service request information according to the basic condition information and the request object.
In one embodiment, the obtaining the service set table includes:
acquiring a service description text;
classifying entities in the target knowledge graph model by adopting the service description text, and determining a service set corresponding to the entities;
and summarizing the entity and the service set corresponding to the entity to obtain the service set table.
In one embodiment, the receiving service request information previously comprises:
acquiring a Wikipedia data set and an initial knowledge graph model;
and establishing the target knowledge map model according to the Wikipedia data set and the initial knowledge map model.
In one embodiment, the building the target knowledge graph model according to the Wikipedia dataset and the initial knowledge graph model comprises:
carrying out named entity identification on the data in the Wikipedia data set to obtain an entity corresponding to the data;
extracting entity relationships of the entities to determine corresponding relationships among the entities;
and inputting the entity and the corresponding relation between the entities into the initial knowledge graph model to generate the target knowledge graph model.
In one embodiment, the obtaining an entity corresponding to the data by performing named entity recognition on the data in the Wikipedia dataset includes:
identifying unstructured data in the Wikipedia data set to obtain unstructured data corresponding to the Wikipedia data set;
and carrying out named entity identification on the unstructured data, and extracting multiple preset types of entities in the unstructured data.
In one embodiment, the extracting entity relationships of the entities, and determining the corresponding relationships between the entities includes:
determining a preset relationship type corresponding to each preset type of entity according to each preset type of entity in the multiple preset types of entities;
and determining the corresponding relation between the entities of each preset type by adopting the preset relation type corresponding to the entity of each preset type.
A cross-domain resource access Internet of things service discovery device based on context awareness computing comprises:
a receiving module, configured to receive service request information;
the entity determining module is used for determining at least one entity in a target knowledge graph model responding to the service request information according to the service request information, wherein the target knowledge graph model represents an entity relationship network formed by the entities and corresponding relationships among the entities;
the first acquisition module is used for acquiring a service set table;
an indexing module, configured to index at least one service set corresponding to the at least one entity in the service set table.
An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method as claimed in any one of the above when executing the program.
A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of the above.
As can be seen from the above description, the method, the apparatus, the electronic device and the non-transitory computer-readable storage medium for discovering the cross-domain resource access internet of things service based on context-aware computing provided by the present invention include: receiving service request information; determining at least one entity in a target knowledge graph model responding to the service request information according to the service request information, wherein the target knowledge graph model represents an entity relationship network formed by the entities and corresponding relationships among the entities; and then acquiring a service set table, and further indexing at least one service set corresponding to the at least one entity in the service set table. The application provides a new service method for the Internet of things facing large-batch, heterogeneous and cross-domain resources, wherein at a service access end, all acquired body knowledge is stored by using a knowledge graph technology so as to acquire entities and the corresponding relation between the entities; in the service access and registration layer, the required service content can be quickly obtained through the service set corresponding to the index entity.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic diagram of an architecture of an SOA internet of things in an embodiment of the prior art;
FIG. 2 is a schematic diagram of a service discovery framework of the Internet of things based on a probabilistic topic model according to an embodiment of the prior art;
fig. 3 is an application environment schematic diagram of a service discovery method for cross-domain resource access to the internet of things based on context-aware computing according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of building a target knowledge graph model according to an embodiment of the present invention;
fig. 5 is a schematic flowchart of a method for discovering cross-domain resource access internet of things service based on context awareness computing according to an embodiment of the present invention;
fig. 6 is a schematic flowchart of a cross-domain resource access internet of things service discovery method based on context awareness computing according to another embodiment of the present invention;
FIG. 7 is a schematic view of a prior art embodiment of a knowledge graph;
fig. 8 is a schematic structural diagram of a cross-domain resource access internet of things service discovery apparatus based on context awareness computing according to an embodiment of the present invention;
fig. 9 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.
It is to be noted that technical terms or scientific terms used in the embodiments of the present invention should have the ordinary meanings as understood by those having ordinary skill in the art to which the present disclosure belongs, unless otherwise defined. The use of "first," "second," and similar terms in this disclosure is not intended to indicate any order, quantity, or importance, but rather is used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
At present, the construction method of the internet of things application system is mainly based on a Service Oriented Architecture (SOA), that is, intelligent articles are regarded as providers and consumers of physical information, functions and resources provided by the intelligent articles and computing, storage and processing resources in the existing internet environment are abstracted into services uniformly, and the services and the resources are integrated as software components of the system according to application requirements to form a networked computing system with loosely coupled cooperative work of an 'object side' and a 'cloud side'.
As shown in fig. 1, in a standard SOA architecture, 3 modules of devices, services and users are mainly included, wherein the main logic functions are all integrated in a service layer. The service layer is divided into a service entity, service registration management, service discovery, service combination and a programming interface from bottom to top. The service entity is abstract embodiment of physical equipment; the service registration management layer is mainly responsible for completing the access of services to the Internet of things according to the protocol and the interface information; the service discovery layer is responsible for searching a proper service in all accessed services according to the service request so as to respond to the request; the service combination layer decomposes a service request from a user into a plurality of service discovery tasks and then issues the tasks to the service discovery layer to wait for the request; the programming interface is an API interface that the service layer opens to the user layer.
According to the standard SOA architecture, various models are correspondingly developed to realize service discovery, such as the Internet of things service discovery architecture based on the probability topic model shown in FIG. 2 (see Weiqiang, Jinzhi, Granyan, Internet of things service discovery based on the probability topic model, software bulletin, 2014, 25 (8): 1640) 1658). However, the prior art model lacks relevant knowledge about the ontology world and has no reasoning function, so that the prior art model is no longer suitable for service discovery of cross-domain and large-scale data.
Since the early 1990 s, context awareness has existed and began to be used as a core feature of ubiquitous and commonly used computer systems. In the past decade, the interest in context-aware computing has evolved from desktop applications, Web applications, mobile computing, and pervasive/pervasive computing to the internet of things. However, with the introduction of the word "ubiquitous computing" in the breakthroughs paper "computer of the 21 st century" published by Mark Weiser in 1991, context-aware computing has become more prevalent. Since then, context-aware research has been established as a prominent research area in the field of computer science. Definitions and explanations have been proposed by many researchers. In 2014, Charith Perera et al set forth the basic concept of context-aware computing and its applicability in solving the understanding problem of the internet of things for access resources. In 2018, Omer BeratSezer discusses the importance of understanding, learning and reasoning of big data on the future success of the Internet of things, and summarizes a plurality of applications of context-aware computing on the technology of the Internet of things again on the basis of CharithPerera, thereby illustrating the good prospect of the research.
The context-aware computing model is classified into 6 types, key value model, label model, graph model, object-based model, logic-based model and ontology knowledge-based model, the ontology knowledge model uses semantic technology to organize contexts into ontologies, many different standards (RDF, RDFS, OW L) and inference functions can be used as required, various development tools and inference engines are provided, however, as the amount of data increases, context retrieval may take up a lot of computing resources and time.
The service discovery method comprises two parts, namely establishing a target knowledge graph model and performing corresponding service by adopting the established target knowledge graph model.
The method for discovering the cross-domain resource access internet of things service based on the context awareness computing can be applied to the application environment shown in fig. 3. The user 101 inputs service request information, the server 102 (equivalent to an SOA architecture) receives the service request information, the server 102 determines at least one entity in a target knowledge graph model responding to the service request information according to the service request information, wherein the target knowledge graph model represents an entity relationship network formed by entities and corresponding relationships among the entities, then a service set table is obtained, and at least one service set corresponding to the at least one entity is indexed in the service set table. The server 102 may be implemented as a stand-alone server or a server cluster composed of a plurality of servers.
Further, the target knowledge graph model established in the application is arranged at an access end of an SOA architecture service discovery layer, and provides service request information for the user 101, wherein the service request information corresponds to at least one response entity in the target knowledge graph; and at a service access and registration layer, indexing at least one service set corresponding to the at least one entity according to the at least one response entity.
In one embodiment, building the target knowledge graph model includes:
step S201: acquiring a Wikipedia data set and an initial knowledge graph model;
step S202: and establishing the target knowledge map model according to the Wikipedia data set and the initial knowledge map model.
The Knowledge map (Knowledge Graph) is called Knowledge domain visualization or Knowledge domain mapping map in the book intelligence world, and is a series of different graphs for displaying the relation between the Knowledge development process and the structure, describing Knowledge resources and carriers thereof by using visualization technology, and mining, analyzing, constructing, drawing and displaying Knowledge and the mutual relation between the Knowledge resources and the carriers.
Specifically, the present application refers the Wikipedia dataset into the initial knowledge graph model to obtain the required target knowledge graph model, with the objective of introducing basic ontology world knowledge to improve the accuracy and practicality of the query service. The ontology world knowledge comprises entities and corresponding relations among the entities.
In one embodiment, the step S202 includes:
step S2021: carrying out named entity identification on the data in the Wikipedia data set to obtain an entity corresponding to the data;
step S2022: extracting entity relationships of the entities to determine corresponding relationships among the entities;
step S2023: and inputting the entity and the corresponding relation between the entities into the initial knowledge graph model to generate the target knowledge graph model.
As shown in fig. 4, the present application uses wikipedia data set for named entity recognition to find multiple entities in unstructured text, then determines the corresponding relationship between the multiple entities, and inputs the multiple entities and the corresponding relationship between each entity into an initial knowledge graph model to obtain a desired target knowledge graph model, and then uses Neo4j database to store our relationship graph. For example, entities in the unstructured text include the Poisten hospital, the people hospital and the collaborating hospital, and all belong to hospitals, then, it is assumed that the corresponding relationship of the Poisten hospital and the people hospital belong to one zone, the corresponding relationship of the people hospital and the collaborating hospital does not belong to one zone, and the corresponding relationship of the Poisten hospital and the collaborating hospital does not belong to one zone. And finally, inputting the entities 'puddle hospital, people hospital and cooperative hospital' and the corresponding relation between the entities 'belonging to one zone, not belonging to one zone and not belonging to one zone' into an initial knowledge graph model to obtain a required target knowledge graph model.
In one embodiment, the step S2021 includes:
step S2021 a: identifying unstructured data in the Wikipedia data set to obtain unstructured data corresponding to the Wikipedia data set;
step S2021 b: and carrying out named entity identification on the unstructured data, and extracting multiple preset types of entities in the unstructured data.
In steps S2021a-S2021b, the unstructured data (i.e., the entities in the unstructured text) are data structures that are irregular or incomplete, have no predefined data model, and are not convenient for representing data in a database two-dimensional logical table.
Further, named entity recognition is performed on the unstructured data to discover multiple entities in unstructured text. The entities in the unstructured text in the above embodiments belong to the same type and are all place names. Of course, entities in unstructured text may also be of multiple types. The plurality of types may include place names, colleges, hospitals, and the like.
In one embodiment, the step S2022 includes:
step S2022 a: determining a preset relationship type corresponding to each preset type of entity according to each preset type of entity in the multiple preset types of entities;
step S2022 b: and determining the corresponding relation between the entities of each preset type by adopting the preset relation type corresponding to the entity of each preset type.
In steps S2022a-S2022b, named entity recognition is performed by using wikipedia data set to find entities in the unstructured text, that is, entities of preset types are extracted from the unstructured text as ontologies in the knowledge base, if it is desired to recognize the preset types of entities as organization names and person names, after recognition by named entities, entities such as XX university, XX company, XX classmate, and XX mr, and the like are found in the unstructured text, and the entities are subjected to relationship classification work. With a preset relationship type relevant for the application domain, as in a geographical location-dependent application domain, subordinated, adjacent, etc. relationships may be used. After the relationship classification is completed, the entities and the relationships between the entities are entered into a knowledge graph, and then our relationship graph is stored using the Neo4j database.
Further, when there are multiple entities of preset types, not only there is a correspondence between multiple entities in each type, but also there may be a correspondence between multiple entities of different types. For example, the unstructured entities include the ponder hospital, the three hospitals of north medicine, the cooperative hospital, the Beijing university, the people university and the Qinghua university, wherein the ponder hospital, the three hospitals of north medicine and the cooperative hospital belong to hospitals, and the Beijing university, the people university and the Qinghua university belong to colleges. The method comprises the steps of setting that a pool hospital and a three-institute of northern medicine do not belong to one area, the three-institute of northern medicine and a cooperation hospital do not belong to one area, and the pool hospital and the cooperation hospital belong to one area, namely, the corresponding relations between the pool hospital and the cooperation hospital are respectively not belong to one area, not belong to one area and belong to one area, namely, whether the preset relation types of the pool hospital, the three-institute of northern medicine and the cooperation hospital belong to the same area or not. The Beijing university is ranked ahead of the Ministry university, the Ministry of Qinghua university and the Beijing university is ranked behind the Ministry of the Qinghua university, namely, the corresponding relations are ranked ahead, the Ministry of Qinghua university and the Ministry of Beijing university, namely, the preset relation types of the Ministry of. In addition, the three northern hospitals and the university of people have adjacent corresponding relations, belong to a region, and also belong to a region. In the process of establishing the target knowledge graph model, a plurality of entities of different types and corresponding relationship types among the entities are all input into the initial knowledge graph model.
As shown in fig. 5 and 6, the service discovery method introducing the target knowledge graph model includes:
step S301: service request information is received.
The service request information refers to information which is input by a user according to personal requirements and is desired to be inquired, wherein the inquiry information can be recognizable information such as texts, pictures and the like. For example, if the user wants to go to the nearest hospital, the user only needs to input "nearest hospital".
Step S302: and determining at least one entity in a target knowledge graph model responding to the service request information according to the service request information, wherein the target knowledge graph model represents an entity relationship network formed by entities and corresponding relationships among the entities.
FIG. 7 is an example of a knowledge graph including a plurality of entities, and entities and associations between entities. Each ellipse represents an entity, and the connecting lines between the ellipses represent the corresponding relation between the entities. For example, entities of the same category are disposed in the same dashed frame, and corresponding relationships exist between entities in the dashed frame, specifically, if an entity person, an identity, a role, and a task in the rightmost dashed frame belong to the same category, and a corresponding relationship exists between entities "person, identity, role, and task", that is, the corresponding relationship between the person and the identity is owned identity, the corresponding relationship between the person and the role is owned role, and the corresponding relationship between the person and the task is owned task, and a plurality of entities of the same category in other dashed frames are similar to the above example, and details are not repeated here. In addition, there may be correspondence between different categories of entities, such as the entity "equipment" in the leftmost dashed box and the entity "person" in the rightmost dashed box, which are devices used.
The target knowledge graph model adopted in the application is a model which is formed by fusing various data and training and can be directly applied to service query, namely, various entities and the relation among the entities are recorded in the target knowledge graph model. When the user enters "nearest me hospital," there is at least one entity in the target knowledge graph model that responds to this request information, at least one entity may be 3 or 5, etc.
Step S303: and acquiring a service set table.
Through the application of a text classification technology, the description texts of the services are classified according to the relevance of each entity, a service set related to each entity node in the target knowledge graph is bound, and a < entity, service set > table is established. If the target knowledge graph has two entities, each service description text is respectively subjected to correlation calculation with the two entities to determine a service set bound by each entity. When the service set to be bound by each entity is determined, a service set table corresponding to each entity can be established.
Specifically, assuming that two entities in the target knowledge graph are "hospital" and "university", respectively, when the user inputs "nearest to me hospital", if two entities in the target knowledge graph model have one entity responding to the request information, namely, entity "hospital". And if the correlation degree calculation is carried out between the service description text and the entity hospital, the service set with the maximum correlation degree with the entity hospital is 'three hospitals in North medicine', and the service set table corresponding to the entity hospital is 'three hospitals in North medicine'.
Step S304: indexing at least one service set corresponding to the at least one entity in the service set table.
Specifically, if the entity in the target knowledge graph is "hospital", the service set corresponding to the entity "hospital" is hospital-related information, and at least includes at least one of a hospital name, a hospital address, a hospital telephone, a route between a hospital and a user, and the like.
Further, when the user enters "nearest to me" hospital, "the entity in the target knowledge-graph that responds to this requested information, namely entity" hospital, "is first determined; then, carrying out relevancy calculation on the entity 'hospital' and the service description text to obtain a service description text with the maximum relevancy as a service set corresponding to the entity 'hospital', namely 'three hospitals in the northern medicine', wherein a formed service set table is 'hospital, three hospitals in the northern medicine'; and finally, acquiring a service set table as 'hospital, three hospitals in North medicine', and searching a service set 'three hospitals in North medicine' corresponding to the entity 'hospital', wherein the service set 'three hospitals in North medicine' is information required by the user.
As shown in fig. 6, the corrected knowledge information is shown in dotted line, but it should be noted that, with the continuous update and expansion of the real-world ontology knowledge, the corresponding relationship between the entities in the target knowledge graph, such as simple numerical relationship and bool value data, can be corrected according to the sensor to meet the user's requirements.
The invention provides a cross-domain resource access Internet of things service discovery method based on context awareness computing, which comprises the following steps: receiving service request information; determining at least one entity in a target knowledge graph model responding to the service request information according to the service request information; and then acquiring a service set table, and further indexing at least one service set corresponding to the at least one entity in the service set table. By providing a new Internet of things system access model facing large-batch, heterogeneous and cross-domain resources, at a service access end, the system uses an information extraction technology to complete extraction of service semantics, organizes ontology knowledge, uses a knowledge map technology to store all acquired ontology knowledge, and provides a knowledge reasoning function; in the service access and registration layer, a text classification technology is applied, all services are classified according to entity nodes in the knowledge graph, and a service set is bound for the entities in the knowledge graph, so that the service discovery level is improved, the service discovery error is reduced, and the service discovery effect is improved.
In one embodiment, the step S302 includes:
step S3021: extracting basic condition information and a request object in the service request information;
step S3022: and determining at least one entity responding to the service request information according to the basic condition information and the request object.
Specifically, in combination with the example of the entities and the corresponding relationship between the entities in the previous embodiment, if in a physical network system providing the geographic location service, the user initiates a service request for the nearest hospital status, that is, the user inputs "nearest hospital to me". After receiving a service request, firstly extracting a request object containing two key points of 'nearest' and 'hospital', and taking the geographical position of a user as basic condition information. Assuming that a user finds an entity node represented by the university of people in a target knowledge graph model at the university of people, searching entity nodes connected by similar "passage" or "adjacent" relations of the entity based on a "recent" requirement, and if the "adjacent" relation exists between the university of people entity and the north three hospital entity, adding a bound service set of the entity nodes into a service set for responding to a request by indexing a < entity, service set > table as an answer of the request.
Furthermore, a parameter e can be set to set knowledge reasoning for the target knowledge graph model in step e to find nodes capable of responding so as to make up for the problem of information loss of the knowledge graph. Where e represents the maximum length of the inference path. For example, the entity node of the three hospitals of north medicine can also be used as a request and response node by connecting the university entity of people and the entity of the china corporation through a 'pass' relationship, and connecting the entity of the china corporation and the entity of the three hospitals of north medicine through a 'pass' relationship.
In one embodiment, the step S303 includes:
step S3031: acquiring a service description text;
step S3032: classifying entities in the target knowledge graph model by adopting the service description text, and determining a service set corresponding to the entities;
step S3033: and summarizing the entity and the service set corresponding to the entity to obtain the service set table.
Specifically, three entities in the target knowledge graph model, such as Beijing university, people university, and Qinghua university, are identified. And after the service is accessed, the service description text and three entities in the target knowledge graph are respectively subjected to relevancy classification. If the service description text is 'camera of Beijing university', the relevance between the service description text and the entity 'Beijing university' is the highest, then the 'camera of Beijing university' is taken as a service set of the entity 'Beijing university'; if an entity is directly embodied in the service description text, the entity is not necessarily classified by using the text relevance, for example, the service description text is 'people university'; if the most relevant entities of the service description text are not in the entity range of the target knowledge graph, such as Tianjin tumor hospital, the text classification is needed to analyze the specific semantic information of the text, and an attempt is made to bind the most relevant entities in the target knowledge graph.
It should be noted that the method of the embodiment of the present invention may be executed by a single device, such as a computer or a server. The method of the embodiment can also be applied to a distributed scene and completed by the mutual cooperation of a plurality of devices. In the case of such a distributed scenario, one of the multiple devices may only perform one or more steps of the method according to the embodiment of the present invention, and the multiple devices interact with each other to complete the method.
As shown in fig. 8, the present application further provides a device for discovering cross-domain resource access internet of things service based on context awareness computing, including:
a receiving module 1, configured to receive service request information;
the entity determining module 2 is configured to determine, according to the service request information, at least one entity in a target knowledge graph model responding to the service request information, where the target knowledge graph model represents an entity relationship network formed by entities and corresponding relationships between the entities;
a first obtaining module 3, configured to obtain a service set table;
an indexing module 4, configured to index at least one service set corresponding to the at least one entity in the service set table.
In one embodiment, the entity determination module 2 comprises:
an extracting module 21, configured to extract basic condition information and a request object in the service request information;
and a target entity determining module 22, configured to determine at least one entity responding to the service request information according to the basic condition information and the request object.
In one embodiment, the first obtaining module 3 includes:
a fourth obtaining module 31, configured to obtain a service description text;
a service set determining module 32, configured to classify, by using the service description text, an entity in the target knowledge graph model, and determine a service set corresponding to the entity;
and a summarizing module 33, configured to summarize the entity and the service set corresponding to the entity to obtain the service set table.
In one embodiment, the receiving module 1 previously comprises:
the second obtaining module 01 is used for obtaining a Wikipedia data set and an initial knowledge graph model;
and the model establishing module 02 is used for establishing the target knowledge map model according to the Wikipedia data set and the initial knowledge map model.
In one embodiment, the model building module 02 includes:
the first identification module 021 is used for conducting named entity identification on the data in the Wikipedia data set to obtain an entity corresponding to the data;
a relationship extraction module 022, configured to perform entity relationship extraction on the entities to determine corresponding relationships between the entities;
a model generating module 023, configured to input the entity and the corresponding relationship between the entities into the initial knowledge graph model, and generate the target knowledge graph model.
In one embodiment, the first identification module 01 includes:
the third obtaining module 011 is configured to identify unstructured data in the Wikipedia dataset to obtain unstructured data corresponding to the Wikipedia dataset;
the second identification module 012 is configured to perform named entity identification on the unstructured data, and extract multiple entities of preset types in the unstructured data.
In one embodiment, the relationship extraction module 022 comprises:
a relationship type determining module 0221, configured to determine, according to an entity of each preset type in the multiple preset types of entities, a preset relationship type corresponding to the entity of each preset type;
a corresponding relationship determining module 0222, configured to determine a corresponding relationship between the entities of each preset type by using the preset relationship type corresponding to the entity of each preset type.
The apparatus of the foregoing embodiment is used to implement the corresponding method in the foregoing embodiment, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
The present application also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor performing the method of:
receiving service request information;
determining at least one entity in a target knowledge graph model responding to the service request information according to the service request information, wherein the target knowledge graph model represents an entity relationship network formed by the entities and corresponding relationships among the entities;
acquiring a service set table;
indexing at least one service set corresponding to the at least one entity in the service set table.
Fig. 9 is a schematic diagram illustrating a more specific hardware structure of an electronic device according to this embodiment, where the electronic device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein the processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 are communicatively coupled to each other within the device via bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random access Memory), a static storage device, a dynamic storage device, or the like. The memory 1020 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present specification is implemented by software or firmware, the relevant program codes are stored in the memory 1020 and called to be executed by the processor 1010.
The input/output interface 1030 is used for connecting an input/output module to input and output information. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 1040 is used for connecting a communication module (not shown in the drawings) to implement communication interaction between the present apparatus and other apparatuses. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).
It should be noted that although the above-mentioned device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040 and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.
The present application further provides a non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of:
receiving service request information;
determining at least one entity in a target knowledge graph model responding to the service request information according to the service request information;
acquiring a service set table;
indexing at least one service set corresponding to the at least one entity in the service set table.
Computer-readable media of the present embodiments, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the idea of the invention, also features in the above embodiments or in different embodiments may be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.
In addition, well known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown within the provided figures for simplicity of illustration and discussion, and so as not to obscure the invention. Furthermore, devices may be shown in block diagram form in order to avoid obscuring the invention, and also in view of the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the present invention is to be implemented (i.e., specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the invention, it should be apparent to one skilled in the art that the invention can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic ram (dram)) may use the discussed embodiments.
The embodiments of the invention are intended to embrace all such alternatives, modifications and variances that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements and the like that may be made without departing from the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. A cross-domain resource access Internet of things service discovery method based on context awareness computing is characterized by comprising the following steps:
receiving service request information;
determining at least one entity in a target knowledge graph model responding to the service request information according to the service request information, wherein the target knowledge graph model represents an entity relationship network formed by the entities and corresponding relationships among the entities;
acquiring a service set table;
indexing at least one service set corresponding to the at least one entity in the service set table.
2. The method of claim 1, wherein the determining, according to the service request information, at least one entity responding to the service request information comprises:
extracting basic condition information and a request object in the service request information;
and determining at least one entity responding to the service request information according to the basic condition information and the request object.
3. The method of claim 1, wherein obtaining the service set table comprises:
acquiring a service description text;
classifying entities in the target knowledge graph model by adopting the service description text, and determining a service set corresponding to the entities;
and summarizing the entity and the service set corresponding to the entity to obtain the service set table.
4. The method of claim 1, wherein the receiving service request information is preceded by:
acquiring a Wikipedia data set and an initial knowledge graph model;
and establishing the target knowledge map model according to the Wikipedia data set and the initial knowledge map model.
5. The method as claimed in claim 4, wherein said building the target knowledge graph model from the Wikipedia dataset and an initial knowledge graph model comprises:
carrying out named entity identification on the data in the Wikipedia data set to obtain an entity corresponding to the data;
extracting entity relationships of the entities to determine corresponding relationships among the entities;
and inputting the entity and the corresponding relation between the entities into the initial knowledge graph model to generate the target knowledge graph model.
6. The method as claimed in claim 5, wherein the identifying named entities for the data in the Wikipedia dataset to obtain the entities corresponding to the data comprises:
identifying unstructured data in the Wikipedia data set to obtain unstructured data corresponding to the Wikipedia data set;
and carrying out named entity identification on the unstructured data, and extracting multiple preset types of entities in the unstructured data.
7. The method of claim 6, wherein the extracting entity relationships of the entities and determining corresponding relationships between the entities comprises:
determining a preset relationship type corresponding to each preset type of entity according to each preset type of entity in the multiple preset types of entities;
and determining the corresponding relation between the entities of each preset type by adopting the preset relation type corresponding to the entity of each preset type.
8. A cross-domain resource access Internet of things service discovery device based on context-aware computing is characterized by comprising:
a receiving module, configured to receive service request information;
the entity determining module is used for determining at least one entity in a target knowledge graph model responding to the service request information according to the service request information, wherein the target knowledge graph model represents an entity relationship network formed by the entities and corresponding relationships among the entities;
the first acquisition module is used for acquiring a service set table;
an indexing module, configured to index at least one service set corresponding to the at least one entity in the service set table.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 7 when executing the program.
10. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1 to 7.
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